Update app.py

app.py

@@ -516,6 +516,20 @@ def predict_stock_prices():
             else:
                 st.error("Failed to fetch stock data. Please try again.")
 
+def calculate_bollinger_bands(data, window=20, num_std=2):
+    rolling_mean = data.rolling(window=window).mean()
+    rolling_std = data.rolling(window=window).std()
+    upper_band = rolling_mean + (rolling_std * num_std)
+    lower_band = rolling_mean - (rolling_std * num_std)
+    return upper_band, lower_band
+
+def calculate_rsi(data, window=14):
+    delta = data.diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
+    rs = gain / loss
+    return 100 - (100 / (1 + rs))
+
 def explore_data():
     st.header("Explore Stock Data")
 
@@ -531,104 +545,203 @@ def explore_data():
 
     if st.button("Explore Data"):
         with st.spinner("Fetching and analyzing data..."):
-            data = yf.download(ticker, period=period)
+            try:
+                data = yf.download(ticker, period=period)
 
-            if data is not None and not data.empty:
-                st.subheader(f"{company_name} Stock Data")
-                
-                # Create tabs for different visualizations
-                tab1, tab2, tab3, tab4, tab5 = st.tabs(["Price History", "OHLC", "Technical Indicators", "Volume & Turnover", "Statistics"])
-                
-                with tab1:
-                    # Stock Price History
-                    fig = go.Figure()
-                    fig.add_trace(go.Scatter(x=data.index, y=data['Open'], mode='lines', name='Open'))
-                    fig.add_trace(go.Scatter(x=data.index, y=data['High'], mode='lines', name='High'))
-                    fig.add_trace(go.Scatter(x=data.index, y=data['Low'], mode='lines', name='Low'))
-                    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close'))
+                if data is not None and not data.empty:
+                    st.subheader(f"{company_name} Stock Data")
                     
-                    # Add rolling mean and standard deviation
-                    data['Rolling_Mean'] = data['Close'].rolling(window=20).mean()
-                    data['Rolling_Std'] = data['Close'].rolling(window=20).std()
-                    fig.add_trace(go.Scatter(x=data.index, y=data['Rolling_Mean'], mode='lines', name='20-day Rolling Mean', line=dict(dash='dash')))
-                    fig.add_trace(go.Scatter(x=data.index, y=data['Rolling_Std'], mode='lines', name='20-day Rolling Std', line=dict(dash='dot')))
+                    # Create tabs for different visualizations
+                    tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs([
+                        "Price History", "OHLC", "Technical Indicators", 
+                        "Volume Analysis", "Returns Distribution", "Moving Averages"
+                    ])
                     
-                    fig.update_layout(title=f"{company_name} Stock Price History",
-                                      xaxis_title="Date",
-                                      yaxis_title="Price",
-                                      hovermode="x unified",
-                                      template="plotly_dark")
-                    st.plotly_chart(fig, use_container_width=True)
-
-                with tab2:
-                    # OHLC Chart
-                    ohlc_fig = go.Figure(data=[go.Candlestick(x=data.index,
-                                                              open=data['Open'],
-                                                              high=data['High'],
-                                                              low=data['Low'],
-                                                              close=data['Close'])])
-                    ohlc_fig.update_layout(title=f"{company_name} OHLC Chart",
-                                           xaxis_title="Date",
-                                           yaxis_title="Price",
-                                           template="plotly_dark",
-                                           xaxis_rangeslider_visible=False)
-                    st.plotly_chart(ohlc_fig, use_container_width=True)
-
-                with tab3:
-                    # Technical Indicators
-                    data['SMA_20'] = SMAIndicator(close=data['Close'], window=20).sma_indicator()
-                    data['EMA_20'] = EMAIndicator(close=data['Close'], window=20).ema_indicator()
-                    bb = BollingerBands(close=data['Close'], window=20, window_dev=2)
-                    data['BB_High'] = bb.bollinger_hband()
-                    data['BB_Low'] = bb.bollinger_lband()
-                    data['RSI'] = RSIIndicator(close=data['Close']).rsi()
-
-                    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
-                                        vertical_spacing=0.03, 
-                                        subplot_titles=("Price and Indicators", "RSI"),
-                                        row_heights=[0.7, 0.3])
-
-                    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close'), row=1, col=1)
-                    fig.add_trace(go.Scatter(x=data.index, y=data['SMA_20'], mode='lines', name='SMA 20'), row=1, col=1)
-                    fig.add_trace(go.Scatter(x=data.index, y=data['EMA_20'], mode='lines', name='EMA 20'), row=1, col=1)
-                    fig.add_trace(go.Scatter(x=data.index, y=data['BB_High'], mode='lines', name='BB High'), row=1, col=1)
-                    fig.add_trace(go.Scatter(x=data.index, y=data['BB_Low'], mode='lines', name='BB Low'), row=1, col=1)
-
-                    fig.add_trace(go.Scatter(x=data.index, y=data['RSI'], mode='lines', name='RSI'), row=2, col=1)
-                    fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1)
-                    fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)
-
-                    fig.update_layout(height=800, title_text=f"{company_name} Technical Indicators",
-                                      hovermode="x unified", template="plotly_dark")
-                    fig.update_xaxes(rangeslider_visible=False, row=2, col=1)
-                    fig.update_yaxes(title_text="Price", row=1, col=1)
-                    fig.update_yaxes(title_text="RSI", row=2, col=1)
-
-                    st.plotly_chart(fig, use_container_width=True)
-
-                with tab4:
-                    # Volume and Turnover
-                    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
-                                        vertical_spacing=0.03, 
-                                        subplot_titles=("Volume", "Turnover (if available)"),
-                                        row_heights=[0.5, 0.5])
-
-                    fig.add_trace(go.Bar(x=data.index, y=data['Volume'], name='Volume'), row=1, col=1)
-                    
-                    if 'Turnover' in data.columns:
-                        fig.add_trace(go.Bar(x=data.index, y=data['Turnover'], name='Turnover'), row=2, col=1)
-                    else:
-                        fig.add_annotation(text="Turnover data not available", xref="paper", yref="paper", x=0.5, y=0.25, showarrow=False)
+                    with tab1:
+                        st.markdown("""
+                        ### Price History
+                        This chart shows the historical closing prices of the stock over time. 
+                        
+                        **How to use**: 
+                        - Use the slider at the bottom to zoom in on specific time periods.
+                        - Hover over the line to see exact prices at different dates.
+                        
+                        **How it helps**: 
+                        - Identify long-term trends in the stock's price.
+                        - Spot key price levels and potential support/resistance areas.
+                        - Understand the overall price movement and volatility of the stock.
+                        """)
+                        
+                        fig = go.Figure()
+                        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close'))
+                        
+                        fig.update_layout(title=f"{company_name} Stock Price History",
+                                          xaxis_title="Date",
+                                          yaxis_title="Price",
+                                          hovermode="x unified",
+                                          template="plotly_dark")
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    with tab2:
+                        st.markdown("""
+                        ### OHLC (Open-High-Low-Close) Chart
+                        This candlestick chart shows the opening, high, low, and closing prices for each trading day.
+                        
+                        **How to use**:
+                        - Green candles indicate days where the closing price was higher than the opening price.
+                        - Red candles indicate days where the closing price was lower than the opening price.
+                        - The thin lines (wicks) show the high and low prices for the day.
+                        
+                        **How it helps**:
+                        - Identify daily price movements and volatility.
+                        - Spot potential reversal patterns or trends.
+                        - Understand the intraday price action of the stock.
+                        """)
+                        
+                        ohlc_fig = go.Figure(data=[go.Candlestick(x=data.index,
+                                                                  open=data['Open'],
+                                                                  high=data['High'],
+                                                                  low=data['Low'],
+                                                                  close=data['Close'])])
+                        ohlc_fig.update_layout(title=f"{company_name} OHLC Chart",
+                                               xaxis_title="Date",
+                                               yaxis_title="Price",
+                                               template="plotly_dark",
+                                               xaxis_rangeslider_visible=False)
+                        st.plotly_chart(ohlc_fig, use_container_width=True)
+
+                    with tab3:
+                        st.markdown("""
+                        ### Technical Indicators
+                        This chart displays various technical indicators alongside the stock price.
+                        
+                        **How to use**:
+                        - The top chart shows the stock price with SMA (Simple Moving Average), EMA (Exponential Moving Average), and Bollinger Bands.
+                        - The bottom chart shows the RSI (Relative Strength Index).
+                        
+                        **How it helps**:
+                        - Identify potential buy/sell signals using moving average crossovers.
+                        - Spot overbought or oversold conditions using RSI.
+                        - Understand price volatility using Bollinger Bands.
+                        - Combine multiple indicators for more robust trading decisions.
+                        """)
+                        
+                        data['SMA_20'] = data['Close'].rolling(window=20).mean()
+                        data['EMA_20'] = data['Close'].ewm(span=20, adjust=False).mean()
+                        data['Upper_BB'], data['Lower_BB'] = calculate_bollinger_bands(data['Close'])
+                        data['RSI'] = calculate_rsi(data['Close'])
+
+                        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
+                                            vertical_spacing=0.03, 
+                                            subplot_titles=("Price and Indicators", "RSI"),
+                                            row_heights=[0.7, 0.3])
+
+                        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close'), row=1, col=1)
+                        fig.add_trace(go.Scatter(x=data.index, y=data['SMA_20'], mode='lines', name='SMA 20'), row=1, col=1)
+                        fig.add_trace(go.Scatter(x=data.index, y=data['EMA_20'], mode='lines', name='EMA 20'), row=1, col=1)
+                        fig.add_trace(go.Scatter(x=data.index, y=data['Upper_BB'], mode='lines', name='Upper BB'), row=1, col=1)
+                        fig.add_trace(go.Scatter(x=data.index, y=data['Lower_BB'], mode='lines', name='Lower BB'), row=1, col=1)
+
+                        fig.add_trace(go.Scatter(x=data.index, y=data['RSI'], mode='lines', name='RSI'), row=2, col=1)
+                        fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1)
+                        fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)
+
+                        fig.update_layout(height=800, title_text=f"{company_name} Technical Indicators",
+                                          hovermode="x unified", template="plotly_dark")
+                        fig.update_xaxes(rangeslider_visible=False, row=2, col=1)
+                        fig.update_yaxes(title_text="Price", row=1, col=1)
+                        fig.update_yaxes(title_text="RSI", row=2, col=1)
+
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    with tab4:
+                        st.markdown("""
+                        ### Volume Analysis
+                        This chart shows the trading volume alongside the stock price.
+                        
+                        **How to use**:
+                        - The top chart displays the daily trading volume.
+                        - The bottom chart shows the corresponding stock price.
+                        
+                        **How it helps**:
+                        - Identify periods of high and low trading activity.
+                        - Confirm price trends (higher volume often validates price movements).
+                        - Spot potential reversals (e.g., high volume at price extremes).
+                        - Understand the liquidity of the stock.
+                        """)
+                        
+                        fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
+                                            vertical_spacing=0.03, 
+                                            subplot_titles=("Volume", "Price"),
+                                            row_heights=[0.7, 0.3])
 
-                    fig.update_layout(height=600, title_text=f"{company_name} Volume and Turnover",
-                                      hovermode="x unified", template="plotly_dark")
-                    fig.update_xaxes(rangeslider_visible=False, row=2, col=1)
-                    fig.update_yaxes(title_text="Volume", row=1, col=1)
-                    fig.update_yaxes(title_text="Turnover", row=2, col=1)
+                        fig.add_trace(go.Bar(x=data.index, y=data['Volume'], name='Volume'), row=1, col=1)
+                        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close Price'), row=2, col=1)
 
-                    st.plotly_chart(fig, use_container_width=True)
+                        fig.update_layout(height=600, title_text=f"{company_name} Volume Analysis",
+                                          hovermode="x unified", template="plotly_dark")
+                        fig.update_xaxes(rangeslider_visible=False, row=2, col=1)
+                        fig.update_yaxes(title_text="Volume", row=1, col=1)
+                        fig.update_yaxes(title_text="Price", row=2, col=1)
+
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    with tab5:
+                        st.markdown("""
+                        ### Returns Distribution
+                        This histogram shows the distribution of daily returns for the stock.
+                        
+                        **How to use**:
+                        - The x-axis represents the daily return percentages.
+                        - The y-axis shows the frequency of each return range.
+                        
+                        **How it helps**:
+                        - Understand the volatility and risk profile of the stock.
+                        - Identify the most common daily return ranges.
+                        - Spot any skewness or unusual patterns in returns.
+                        - Compare the stock's return distribution to a normal distribution for risk assessment.
+                        """)
+                        
+                        returns = data['Close'].pct_change().dropna()
+                        fig = go.Figure(data=[go.Histogram(x=returns, nbinsx=50)])
+                        fig.update_layout(title=f"{company_name} Returns Distribution",
+                                          xaxis_title="Daily Returns",
+                                          yaxis_title="Frequency",
+                                          template="plotly_dark")
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    with tab6:
+                        st.markdown("""
+                        ### Moving Averages
+                        This chart shows the stock price along with 50-day and 200-day Simple Moving Averages (SMA).
+                        
+                        **How to use**:
+                        - The blue line represents the stock's closing price.
+                        - The orange line is the 50-day SMA.
+                        - The green line is the 200-day SMA.
+                        
+                        **How it helps**:
+                        - Identify long-term trends in the stock price.
+                        - Spot potential buy/sell signals when the price crosses above/below the moving averages.
+                        - Recognize "Golden Cross" (50-day crosses above 200-day) and "Death Cross" (50-day crosses below 200-day) signals.
+                        - Understand the overall momentum of the stock.
+                        """)
+                        
+                        data['SMA_50'] = data['Close'].rolling(window=50).mean()
+                        data['SMA_200'] = data['Close'].rolling(window=200).mean()
+
+                        fig = go.Figure()
+                        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close'))
+                        fig.add_trace(go.Scatter(x=data.index, y=data['SMA_50'], mode='lines', name='50-day SMA'))
+                        fig.add_trace(go.Scatter(x=data.index, y=data['SMA_200'], mode='lines', name='200-day SMA'))
+
+                        fig.update_layout(title=f"{company_name} Moving Averages",
+                                          xaxis_title="Date",
+                                          yaxis_title="Price",
+                                          template="plotly_dark",
+                                          hovermode="x unified")
+                        st.plotly_chart(fig, use_container_width=True)
 
-                with tab5:
                     # Display key statistics
                     st.subheader("Key Statistics")
                     col1, col2, col3 = st.columns(3)
@@ -643,20 +756,10 @@ def explore_data():
                         st.metric("Avg Daily Return", f"{returns.mean():.2f}%")
                         st.metric("Return Volatility", f"{returns.std():.2f}%")
 
-                    # Correlation Heatmap
-                    correlation = data[['Open', 'High', 'Low', 'Close', 'Volume']].corr()
-                    heatmap_fig = px.imshow(correlation, text_auto=True, aspect="auto", color_continuous_scale='Viridis')
-                    heatmap_fig.update_layout(title="Correlation Heatmap", template="plotly_dark")
-                    st.plotly_chart(heatmap_fig, use_container_width=True)
-
-                # Display news
-                st.subheader("Latest News")
-                news = fetch_news(company_name)
-                for item in news:
-                    st.markdown(f"[{item['title']}]({item['link']}) ({item['pubDate']})")
-
-            else:
-                st.error("Failed to fetch data. Please try again.")
+                else:
+                    st.error("Failed to fetch data. Please try again.")
+            except Exception as e:
+                st.error(f"An error occurred: {str(e)}. Please try a different time period or check your internet connection.")
 
 if __name__ == "__main__":
     main()